Popularity prediction-based caching in content delivery networks

In content delivery networks (CDNs), caches are resources that must be allocated. For that purpose, videos' popularity knowledge helps to make efficient decisions about which videos should be cached. Thus, we must be able to anticipate future needs in terms of requested videos. To do this, we rely on the requests history. This paper focuses on predicting the videos' popularity: the daily number of requests. For that purpose, we propose a two-level prediction approach. At the first level, the experts compute the videos' popularity, each expert using its own prediction method with its own parameters. At the second level, the forecasters select the best experts and build a prediction based on the predictions provided by these experts. The prediction accuracy is evaluated by a loss function as the discrepancy between the prediction value and the real number of requests. We use real traces extracted from YouTube to compare different prediction methods and determine the best parameter tuning for experts and forecasters. The goal is to find the best trade-off between complexity and accuracy of the prediction methods used. Finally, we apply these prediction methods to caching. Prediction methods are compared in terms of cache hit ratio and update ratio. The gain brought by this two-level prediction approach is compared with that obtained by a single prediction level. The results show that the choice of a two-level prediction approach is justified.